Title :
Design of digital differentiators and Hilbert transformers by feedback neural networks
Author :
Bhattacharya, D. ; Antoniou, A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Victoria Univ., BC, Canada
Abstract :
A Hopfield-type neural network is proposed for the design of nonrecursive digital differentiators and Hilbert transformers. Given the amplitude response, the all-analog network computes the filter coefficients in real time. The network is simulated and a few examples are included to show that this is an efficient way of solving the approximation problem and has a high potential for implementation in analog VLSI
Keywords :
FIR filters; Hilbert transforms; Hopfield neural nets; VLSI; analogue processing circuits; delay circuits; differentiating circuits; filtering theory; Hilbert transformers; Hopfield-type neural network; all-analog network; amplitude response; analog VLSI; approximation problem solution; digital differentiators design; feedback neural networks; filter coefficients; nonrecursive digital differentiators; Computer networks; Cost function; Finite impulse response filter; Frequency; Hopfield neural networks; Neural networks; Neurofeedback; Neurons; Transformers; Very large scale integration;
Conference_Titel :
Communications, Computers, and Signal Processing, 1995. Proceedings., IEEE Pacific Rim Conference on
Conference_Location :
Victoria, BC
Print_ISBN :
0-7803-2553-2
DOI :
10.1109/PACRIM.1995.519576
